Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 MiB
Average record size in memory309.5 B

Variable types

Text1
Numeric14
Categorical3

Alerts

avg_monthly_spend is highly overall correlated with spend_credit_score_interaction and 1 other fieldsHigh correlation
credit_card_limit is highly overall correlated with spend_to_limit_ratioHigh correlation
debt_to_income_ratio is highly overall correlated with loan_balance and 1 other fieldsHigh correlation
income is highly overall correlated with pca1High correlation
loan_balance is highly overall correlated with debt_to_income_ratio and 1 other fieldsHigh correlation
loan_balance_log is highly overall correlated with debt_to_income_ratio and 1 other fieldsHigh correlation
missed_payments is highly overall correlated with pca1High correlation
pca1 is highly overall correlated with income and 1 other fieldsHigh correlation
spend_credit_score_interaction is highly overall correlated with avg_monthly_spend and 1 other fieldsHigh correlation
spend_to_limit_ratio is highly overall correlated with avg_monthly_spend and 2 other fieldsHigh correlation
spend_to_limit_ratio is highly skewed (γ1 = -94.69734502) Skewed
debt_to_income_ratio is highly skewed (γ1 = 40.56501671) Skewed
customer_id has unique values Unique
pca1 has unique values Unique
pca2 has unique values Unique
spend_to_limit_ratio has unique values Unique
debt_to_income_ratio has unique values Unique
num_credit_cards has 1340 (13.4%) zeros Zeros
missed_payments has 2210 (22.1%) zeros Zeros

Reproduction

Analysis started2025-04-14 16:49:34.122681
Analysis finished2025-04-14 16:49:48.923688
Duration14.8 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

customer_id
Text

Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size644.7 KiB
2025-04-14T22:19:49.172635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters90000
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st rowCUST00000
2nd rowCUST00001
3rd rowCUST00002
4th rowCUST00003
5th rowCUST00004
ValueCountFrequency (%)
cust00000 1
 
< 0.1%
cust00008 1
 
< 0.1%
cust00017 1
 
< 0.1%
cust00002 1
 
< 0.1%
cust00003 1
 
< 0.1%
cust00004 1
 
< 0.1%
cust00005 1
 
< 0.1%
cust00006 1
 
< 0.1%
cust00007 1
 
< 0.1%
cust00009 1
 
< 0.1%
Other values (9990) 9990
99.9%
2025-04-14T22:19:49.528320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 14000
15.6%
C 10000
11.1%
U 10000
11.1%
S 10000
11.1%
T 10000
11.1%
6 4000
 
4.4%
7 4000
 
4.4%
3 4000
 
4.4%
4 4000
 
4.4%
5 4000
 
4.4%
Other values (4) 16000
17.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14000
15.6%
C 10000
11.1%
U 10000
11.1%
S 10000
11.1%
T 10000
11.1%
6 4000
 
4.4%
7 4000
 
4.4%
3 4000
 
4.4%
4 4000
 
4.4%
5 4000
 
4.4%
Other values (4) 16000
17.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14000
15.6%
C 10000
11.1%
U 10000
11.1%
S 10000
11.1%
T 10000
11.1%
6 4000
 
4.4%
7 4000
 
4.4%
3 4000
 
4.4%
4 4000
 
4.4%
5 4000
 
4.4%
Other values (4) 16000
17.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14000
15.6%
C 10000
11.1%
U 10000
11.1%
S 10000
11.1%
T 10000
11.1%
6 4000
 
4.4%
7 4000
 
4.4%
3 4000
 
4.4%
4 4000
 
4.4%
5 4000
 
4.4%
Other values (4) 16000
17.8%

age
Real number (ℝ)

Distinct52
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.5394
Minimum18
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:19:49.631372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q131
median43
Q356
95-th percentile67
Maximum69
Range51
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.911636
Coefficient of variation (CV)0.34248602
Kurtosis-1.1809278
Mean43.5394
Median Absolute Deviation (MAD)13
Skewness0.0020168078
Sum435394
Variance222.35688
MonotonicityNot monotonic
2025-04-14T22:19:49.728037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43 227
 
2.3%
62 219
 
2.2%
66 218
 
2.2%
40 216
 
2.2%
34 213
 
2.1%
52 212
 
2.1%
64 211
 
2.1%
45 211
 
2.1%
38 206
 
2.1%
35 205
 
2.1%
Other values (42) 7862
78.6%
ValueCountFrequency (%)
18 177
1.8%
19 201
2.0%
20 191
1.9%
21 196
2.0%
22 189
1.9%
23 190
1.9%
24 163
1.6%
25 192
1.9%
26 181
1.8%
27 176
1.8%
ValueCountFrequency (%)
69 181
1.8%
68 196
2.0%
67 178
1.8%
66 218
2.2%
65 178
1.8%
64 211
2.1%
63 172
1.7%
62 219
2.2%
61 192
1.9%
60 167
1.7%

income
Real number (ℝ)

High correlation 

Distinct9986
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59996.199
Minimum-18448.01
Maximum130581.1
Zeros0
Zeros (%)0.0%
Negative16
Negative (%)0.2%
Memory size78.2 KiB
2025-04-14T22:19:50.028708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-18448.01
5-th percentile26952.882
Q146564.598
median59942.99
Q373532.582
95-th percentile92779.506
Maximum130581.1
Range149029.11
Interquartile range (IQR)26967.985

Descriptive statistics

Standard deviation20092.986
Coefficient of variation (CV)0.33490431
Kurtosis0.035065369
Mean59996.199
Median Absolute Deviation (MAD)13510.915
Skewness-0.00043851986
Sum5.9996199 × 108
Variance4.0372808 × 108
MonotonicityNot monotonic
2025-04-14T22:19:50.123307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44487.87 2
 
< 0.1%
62367 2
 
< 0.1%
52487.33 2
 
< 0.1%
52659.55 2
 
< 0.1%
60467.7 2
 
< 0.1%
63028.15 2
 
< 0.1%
79888.29 2
 
< 0.1%
78438.73 2
 
< 0.1%
62734.93 2
 
< 0.1%
58965.74 2
 
< 0.1%
Other values (9976) 9980
99.8%
ValueCountFrequency (%)
-18448.01 1
< 0.1%
-16733.11 1
< 0.1%
-13767.31 1
< 0.1%
-12021.7 1
< 0.1%
-7511.58 1
< 0.1%
-6590.08 1
< 0.1%
-6422.29 1
< 0.1%
-5006.67 1
< 0.1%
-4830.28 1
< 0.1%
-4651.31 1
< 0.1%
ValueCountFrequency (%)
130581.1 1
< 0.1%
128578.21 1
< 0.1%
127555.36 1
< 0.1%
127547.66 1
< 0.1%
125755.22 1
< 0.1%
125714.47 1
< 0.1%
125682.36 1
< 0.1%
123731.5 1
< 0.1%
123155.43 1
< 0.1%
122353.62 1
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size639.8 KiB
Employed
5962 
Freelancer
1976 
Unemployed
1051 
Retired
1011 

Length

Max length10
Median length8
Mean length8.5043
Min length7

Characters and Unicode

Total characters85043
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnemployed
2nd rowFreelancer
3rd rowFreelancer
4th rowUnemployed
5th rowRetired

Common Values

ValueCountFrequency (%)
Employed 5962
59.6%
Freelancer 1976
 
19.8%
Unemployed 1051
 
10.5%
Retired 1011
 
10.1%

Length

2025-04-14T22:19:50.218150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:19:50.288029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
employed 5962
59.6%
freelancer 1976
 
19.8%
unemployed 1051
 
10.5%
retired 1011
 
10.1%

Most occurring characters

ValueCountFrequency (%)
e 16014
18.8%
l 8989
10.6%
d 8024
9.4%
p 7013
8.2%
o 7013
8.2%
y 7013
8.2%
m 7013
8.2%
E 5962
 
7.0%
r 4963
 
5.8%
n 3027
 
3.6%
Other values (7) 10012
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 85043
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 16014
18.8%
l 8989
10.6%
d 8024
9.4%
p 7013
8.2%
o 7013
8.2%
y 7013
8.2%
m 7013
8.2%
E 5962
 
7.0%
r 4963
 
5.8%
n 3027
 
3.6%
Other values (7) 10012
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 85043
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 16014
18.8%
l 8989
10.6%
d 8024
9.4%
p 7013
8.2%
o 7013
8.2%
y 7013
8.2%
m 7013
8.2%
E 5962
 
7.0%
r 4963
 
5.8%
n 3027
 
3.6%
Other values (7) 10012
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 85043
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 16014
18.8%
l 8989
10.6%
d 8024
9.4%
p 7013
8.2%
o 7013
8.2%
y 7013
8.2%
m 7013
8.2%
E 5962
 
7.0%
r 4963
 
5.8%
n 3027
 
3.6%
Other values (7) 10012
11.8%

credit_card_limit
Real number (ℝ)

High correlation 

Distinct9953
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9972.4282
Minimum-3396.81
Maximum21074.87
Zeros0
Zeros (%)0.0%
Negative6
Negative (%)0.1%
Memory size78.2 KiB
2025-04-14T22:19:50.369947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-3396.81
5-th percentile5118.9795
Q17905.7675
median9992.105
Q312022.95
95-th percentile14938.139
Maximum21074.87
Range24471.68
Interquartile range (IQR)4117.1825

Descriptive statistics

Standard deviation2990.1251
Coefficient of variation (CV)0.29983922
Kurtosis-0.009431925
Mean9972.4282
Median Absolute Deviation (MAD)2053.71
Skewness0.0076802263
Sum99724282
Variance8940848.3
MonotonicityNot monotonic
2025-04-14T22:19:50.459685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8707.97 2
 
< 0.1%
11461.03 2
 
< 0.1%
11905.39 2
 
< 0.1%
11930.92 2
 
< 0.1%
12444.96 2
 
< 0.1%
7638.56 2
 
< 0.1%
15081.87 2
 
< 0.1%
11286.32 2
 
< 0.1%
13682.38 2
 
< 0.1%
6837.55 2
 
< 0.1%
Other values (9943) 9980
99.8%
ValueCountFrequency (%)
-3396.81 1
< 0.1%
-598.45 1
< 0.1%
-485.14 1
< 0.1%
-360.06 1
< 0.1%
-282.73 1
< 0.1%
-18.07 1
< 0.1%
389.96 1
< 0.1%
522.74 1
< 0.1%
720.32 1
< 0.1%
749.61 1
< 0.1%
ValueCountFrequency (%)
21074.87 1
< 0.1%
20063.72 1
< 0.1%
20044.62 1
< 0.1%
19792.87 1
< 0.1%
19646.12 1
< 0.1%
19476.83 1
< 0.1%
19474.22 1
< 0.1%
19410.06 1
< 0.1%
19396.98 1
< 0.1%
19114.84 1
< 0.1%

num_credit_cards
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0002
Minimum0
Maximum10
Zeros1340
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:19:50.533822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4121612
Coefficient of variation (CV)0.70601002
Kurtosis0.49196383
Mean2.0002
Median Absolute Deviation (MAD)1
Skewness0.70571991
Sum20002
Variance1.9941994
MonotonicityNot monotonic
2025-04-14T22:19:50.603548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 2730
27.3%
2 2694
26.9%
3 1812
18.1%
0 1340
13.4%
4 888
 
8.9%
5 376
 
3.8%
6 118
 
1.2%
7 32
 
0.3%
8 7
 
0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
0 1340
13.4%
1 2730
27.3%
2 2694
26.9%
3 1812
18.1%
4 888
 
8.9%
5 376
 
3.8%
6 118
 
1.2%
7 32
 
0.3%
8 7
 
0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9 2
 
< 0.1%
8 7
 
0.1%
7 32
 
0.3%
6 118
 
1.2%
5 376
 
3.8%
4 888
 
8.9%
3 1812
18.1%
2 2694
26.9%
1 2730
27.3%

credit_score
Real number (ℝ)

Distinct308
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean680.7964
Minimum472
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:19:50.683540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum472
5-th percentile599
Q1646
median681
Q3715
95-th percentile763
Maximum850
Range378
Interquartile range (IQR)69

Descriptive statistics

Standard deviation50.272319
Coefficient of variation (CV)0.073843398
Kurtosis-0.049016738
Mean680.7964
Median Absolute Deviation (MAD)34
Skewness0.00014289622
Sum6807964
Variance2527.3061
MonotonicityNot monotonic
2025-04-14T22:19:50.778752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
684 90
 
0.9%
687 90
 
0.9%
690 89
 
0.9%
681 87
 
0.9%
688 84
 
0.8%
665 84
 
0.8%
666 83
 
0.8%
686 83
 
0.8%
691 82
 
0.8%
676 82
 
0.8%
Other values (298) 9146
91.5%
ValueCountFrequency (%)
472 1
 
< 0.1%
494 1
 
< 0.1%
514 1
 
< 0.1%
520 2
< 0.1%
521 1
 
< 0.1%
522 1
 
< 0.1%
526 1
 
< 0.1%
528 3
< 0.1%
533 1
 
< 0.1%
535 2
< 0.1%
ValueCountFrequency (%)
850 5
0.1%
843 1
 
< 0.1%
833 1
 
< 0.1%
832 1
 
< 0.1%
831 1
 
< 0.1%
830 1
 
< 0.1%
829 1
 
< 0.1%
828 2
 
< 0.1%
827 1
 
< 0.1%
825 1
 
< 0.1%

loan_balance
Real number (ℝ)

High correlation 

Distinct9975
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14877.974
Minimum1.51
Maximum176120.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:19:50.872393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.51
5-th percentile747.0815
Q14134.1125
median10335.495
Q320732.778
95-th percentile44650.295
Maximum176120.56
Range176119.05
Interquartile range (IQR)16598.665

Descriptive statistics

Standard deviation14985.761
Coefficient of variation (CV)1.0072447
Kurtosis6.8351109
Mean14877.974
Median Absolute Deviation (MAD)7308.465
Skewness2.0583806
Sum1.4877974 × 108
Variance2.2457305 × 108
MonotonicityNot monotonic
2025-04-14T22:19:50.966279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15211.71 2
 
< 0.1%
212.75 2
 
< 0.1%
12434.56 2
 
< 0.1%
5500.28 2
 
< 0.1%
2231.51 2
 
< 0.1%
15596 2
 
< 0.1%
2390.65 2
 
< 0.1%
506.68 2
 
< 0.1%
1147.37 2
 
< 0.1%
13141.43 2
 
< 0.1%
Other values (9965) 9980
99.8%
ValueCountFrequency (%)
1.51 1
< 0.1%
1.93 1
< 0.1%
2.11 1
< 0.1%
2.44 1
< 0.1%
2.7 1
< 0.1%
5.28 1
< 0.1%
7.24 1
< 0.1%
8.14 1
< 0.1%
9.51 1
< 0.1%
12.64 1
< 0.1%
ValueCountFrequency (%)
176120.56 1
< 0.1%
127465.71 1
< 0.1%
124666.25 1
< 0.1%
121852.79 1
< 0.1%
120146.11 1
< 0.1%
118257.58 1
< 0.1%
115592.23 1
< 0.1%
115350.15 1
< 0.1%
111674.53 1
< 0.1%
110958.48 1
< 0.1%

avg_monthly_spend
Real number (ℝ)

High correlation 

Distinct9766
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1800.8887
Minimum-877.78
Maximum3963.35
Zeros0
Zeros (%)0.0%
Negative15
Negative (%)0.1%
Memory size78.2 KiB
2025-04-14T22:19:51.060496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-877.78
5-th percentile792.4695
Q11399.625
median1800.89
Q32206.735
95-th percentile2822.5975
Maximum3963.35
Range4841.13
Interquartile range (IQR)807.11

Descriptive statistics

Standard deviation607.11077
Coefficient of variation (CV)0.33711732
Kurtosis-0.030312913
Mean1800.8887
Median Absolute Deviation (MAD)403.07
Skewness-0.0016729499
Sum18008887
Variance368583.49
MonotonicityNot monotonic
2025-04-14T22:19:51.154751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1364.18 3
 
< 0.1%
1719.72 3
 
< 0.1%
1884.29 3
 
< 0.1%
1483.18 3
 
< 0.1%
1434.7 2
 
< 0.1%
1747.06 2
 
< 0.1%
1989.85 2
 
< 0.1%
2129.81 2
 
< 0.1%
1680.26 2
 
< 0.1%
2238.68 2
 
< 0.1%
Other values (9756) 9976
99.8%
ValueCountFrequency (%)
-877.78 1
< 0.1%
-564.01 1
< 0.1%
-285.02 1
< 0.1%
-256.19 1
< 0.1%
-239 1
< 0.1%
-184.43 1
< 0.1%
-117.57 1
< 0.1%
-109.83 1
< 0.1%
-89.9 1
< 0.1%
-84.47 1
< 0.1%
ValueCountFrequency (%)
3963.35 1
< 0.1%
3863.23 1
< 0.1%
3835.05 1
< 0.1%
3830.24 1
< 0.1%
3713.55 1
< 0.1%
3703.35 1
< 0.1%
3699.43 1
< 0.1%
3682.22 1
< 0.1%
3675.28 1
< 0.1%
3645.4 1
< 0.1%

missed_payments
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4909
Minimum0
Maximum8
Zeros2210
Zeros (%)22.1%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:19:51.229812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2155924
Coefficient of variation (CV)0.81534136
Kurtosis0.68499405
Mean1.4909
Median Absolute Deviation (MAD)1
Skewness0.82812407
Sum14909
Variance1.477665
MonotonicityNot monotonic
2025-04-14T22:19:51.305322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 3417
34.2%
2 2514
25.1%
0 2210
22.1%
3 1202
 
12.0%
4 479
 
4.8%
5 138
 
1.4%
6 29
 
0.3%
7 10
 
0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 2210
22.1%
1 3417
34.2%
2 2514
25.1%
3 1202
 
12.0%
4 479
 
4.8%
5 138
 
1.4%
6 29
 
0.3%
7 10
 
0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 10
 
0.1%
6 29
 
0.3%
5 138
 
1.4%
4 479
 
4.8%
3 1202
 
12.0%
2 2514
25.1%
1 3417
34.2%
0 2210
22.1%

region
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size605.4 KiB
East
2063 
West
2012 
North
2012 
South
1987 
Central
1926 

Length

Max length7
Median length5
Mean length4.9777
Min length4

Characters and Unicode

Total characters49777
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWest
2nd rowEast
3rd rowCentral
4th rowWest
5th rowSouth

Common Values

ValueCountFrequency (%)
East 2063
20.6%
West 2012
20.1%
North 2012
20.1%
South 1987
19.9%
Central 1926
19.3%

Length

2025-04-14T22:19:51.388780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:19:51.457647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
east 2063
20.6%
west 2012
20.1%
north 2012
20.1%
south 1987
19.9%
central 1926
19.3%

Most occurring characters

ValueCountFrequency (%)
t 10000
20.1%
s 4075
8.2%
o 3999
 
8.0%
h 3999
 
8.0%
a 3989
 
8.0%
e 3938
 
7.9%
r 3938
 
7.9%
E 2063
 
4.1%
W 2012
 
4.0%
N 2012
 
4.0%
Other values (5) 9752
19.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49777
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 10000
20.1%
s 4075
8.2%
o 3999
 
8.0%
h 3999
 
8.0%
a 3989
 
8.0%
e 3938
 
7.9%
r 3938
 
7.9%
E 2063
 
4.1%
W 2012
 
4.0%
N 2012
 
4.0%
Other values (5) 9752
19.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49777
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 10000
20.1%
s 4075
8.2%
o 3999
 
8.0%
h 3999
 
8.0%
a 3989
 
8.0%
e 3938
 
7.9%
r 3938
 
7.9%
E 2063
 
4.1%
W 2012
 
4.0%
N 2012
 
4.0%
Other values (5) 9752
19.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49777
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 10000
20.1%
s 4075
8.2%
o 3999
 
8.0%
h 3999
 
8.0%
a 3989
 
8.0%
e 3938
 
7.9%
r 3938
 
7.9%
E 2063
 
4.1%
W 2012
 
4.0%
N 2012
 
4.0%
Other values (5) 9752
19.6%

loan_balance_log
Real number (ℝ)

High correlation 

Distinct9975
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.019635
Minimum0.92028275
Maximum12.07893
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:19:51.543599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.92028275
5-th percentile6.6175116
Q18.3272698
median9.2434361
Q39.9395194
95-th percentile10.706639
Maximum12.07893
Range11.158647
Interquartile range (IQR)1.6122496

Descriptive statistics

Standard deviation1.2975799
Coefficient of variation (CV)0.14386169
Kurtosis2.259858
Mean9.019635
Median Absolute Deviation (MAD)0.78164912
Skewness-1.1307792
Sum90196.35
Variance1.6837137
MonotonicityNot monotonic
2025-04-14T22:19:51.634028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.629886542 2
 
< 0.1%
5.364807108 2
 
< 0.1%
9.428315389 2
 
< 0.1%
8.612736071 2
 
< 0.1%
7.710881792 2
 
< 0.1%
9.654833867 2
 
< 0.1%
7.779738783 2
 
< 0.1%
6.229851328 2
 
< 0.1%
7.046098825 2
 
< 0.1%
9.483601206 2
 
< 0.1%
Other values (9965) 9980
99.8%
ValueCountFrequency (%)
0.9202827531 1
< 0.1%
1.075002423 1
< 0.1%
1.134622726 1
< 0.1%
1.235471471 1
< 0.1%
1.30833282 1
< 0.1%
1.83736998 1
< 0.1%
2.109000344 1
< 0.1%
2.212660385 1
< 0.1%
2.352327185 1
< 0.1%
2.613006652 1
< 0.1%
ValueCountFrequency (%)
12.07892972 1
< 0.1%
11.75561051 1
< 0.1%
11.73340347 1
< 0.1%
11.71057716 1
< 0.1%
11.69647219 1
< 0.1%
11.68062886 1
< 0.1%
11.65783267 1
< 0.1%
11.65573623 1
< 0.1%
11.62335289 1
< 0.1%
11.61692037 1
< 0.1%

pca1
Real number (ℝ)

High correlation  Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.8159701 × 10-18
Minimum-4.1748279
Maximum4.2730381
Zeros0
Zeros (%)0.0%
Negative5090
Negative (%)50.9%
Memory size78.2 KiB
2025-04-14T22:19:51.724297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-4.1748279
5-th percentile-1.6199741
Q1-0.69604755
median-0.023379362
Q30.68756774
95-th percentile1.7024509
Maximum4.2730381
Range8.447866
Interquartile range (IQR)1.3836153

Descriptive statistics

Standard deviation1.0183696
Coefficient of variation (CV)-1.3029343 × 1017
Kurtosis0.14564389
Mean-7.8159701 × 10-18
Median Absolute Deviation (MAD)0.69020032
Skewness0.12961845
Sum7.1054274 × 10-15
Variance1.0370766
MonotonicityNot monotonic
2025-04-14T22:19:51.815173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.210173851 1
 
< 0.1%
-0.005025252127 1
 
< 0.1%
-0.5225779145 1
 
< 0.1%
0.6381900492 1
 
< 0.1%
1.596159711 1
 
< 0.1%
-0.958760143 1
 
< 0.1%
1.042856183 1
 
< 0.1%
-1.810144415 1
 
< 0.1%
-0.7222099121 1
 
< 0.1%
-1.357563637 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
-4.174827876 1
< 0.1%
-3.714249239 1
< 0.1%
-3.68035998 1
< 0.1%
-3.533007898 1
< 0.1%
-3.351111675 1
< 0.1%
-3.322100361 1
< 0.1%
-3.306036454 1
< 0.1%
-3.301199625 1
< 0.1%
-3.271621491 1
< 0.1%
-3.147857749 1
< 0.1%
ValueCountFrequency (%)
4.27303808 1
< 0.1%
4.03271911 1
< 0.1%
3.93765415 1
< 0.1%
3.795168638 1
< 0.1%
3.790700504 1
< 0.1%
3.698110751 1
< 0.1%
3.584267974 1
< 0.1%
3.553059477 1
< 0.1%
3.483948061 1
< 0.1%
3.448801841 1
< 0.1%

pca2
Real number (ℝ)

Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.6342483 × 10-17
Minimum-3.8935813
Maximum4.1699017
Zeros0
Zeros (%)0.0%
Negative5063
Negative (%)50.6%
Memory size78.2 KiB
2025-04-14T22:19:51.905135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-3.8935813
5-th percentile-1.6136636
Q1-0.68003503
median-0.015310016
Q30.65609536
95-th percentile1.6925444
Maximum4.1699017
Range8.063483
Interquartile range (IQR)1.3361304

Descriptive statistics

Standard deviation1.0127757
Coefficient of variation (CV)-6.1971957 × 1016
Kurtosis0.20130346
Mean-1.6342483 × 10-17
Median Absolute Deviation (MAD)0.6688737
Skewness0.12458755
Sum-1.4210855 × 10-13
Variance1.0257145
MonotonicityNot monotonic
2025-04-14T22:19:52.004260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.728224002 1
 
< 0.1%
-0.2737628105 1
 
< 0.1%
-2.947089522 1
 
< 0.1%
1.560447233 1
 
< 0.1%
0.2203495728 1
 
< 0.1%
0.3427847577 1
 
< 0.1%
-0.06437472275 1
 
< 0.1%
0.598457478 1
 
< 0.1%
0.9183522908 1
 
< 0.1%
-0.3313555229 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
-3.893581329 1
< 0.1%
-3.566164472 1
< 0.1%
-3.374005363 1
< 0.1%
-3.142957747 1
< 0.1%
-3.142750545 1
< 0.1%
-3.129998414 1
< 0.1%
-3.118694452 1
< 0.1%
-3.056756864 1
< 0.1%
-3.04136719 1
< 0.1%
-3.024853333 1
< 0.1%
ValueCountFrequency (%)
4.169901692 1
< 0.1%
3.891479469 1
< 0.1%
3.826228206 1
< 0.1%
3.628458344 1
< 0.1%
3.619316327 1
< 0.1%
3.603268093 1
< 0.1%
3.568688902 1
< 0.1%
3.495234507 1
< 0.1%
3.441559415 1
< 0.1%
3.426973589 1
< 0.1%

cluster
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
2
2991 
0
2895 
1
2822 
3
1292 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row2
4th row3
5th row1

Common Values

ValueCountFrequency (%)
2 2991
29.9%
0 2895
28.9%
1 2822
28.2%
3 1292
12.9%

Length

2025-04-14T22:19:52.091975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:19:52.155237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 2991
29.9%
0 2895
28.9%
1 2822
28.2%
3 1292
12.9%

Most occurring characters

ValueCountFrequency (%)
2 2991
29.9%
0 2895
28.9%
1 2822
28.2%
3 1292
12.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2991
29.9%
0 2895
28.9%
1 2822
28.2%
3 1292
12.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2991
29.9%
0 2895
28.9%
1 2822
28.2%
3 1292
12.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2991
29.9%
0 2895
28.9%
1 2822
28.2%
3 1292
12.9%

spend_to_limit_ratio
Real number (ℝ)

High correlation  Skewed  Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19427624
Minimum-86.49092
Maximum4.3803714
Zeros0
Zeros (%)0.0%
Negative21
Negative (%)0.2%
Memory size78.2 KiB
2025-04-14T22:19:52.235325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-86.49092
5-th percentile0.073709686
Q10.13254135
median0.17905158
Q30.24505953
95-th percentile0.39514019
Maximum4.3803714
Range90.871291
Interquartile range (IQR)0.11251818

Descriptive statistics

Standard deviation0.88294653
Coefficient of variation (CV)4.5447993
Kurtosis9294.712
Mean0.19427624
Median Absolute Deviation (MAD)0.054050016
Skewness-94.697345
Sum1942.7624
Variance0.77959457
MonotonicityNot monotonic
2025-04-14T22:19:52.324402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2373240139 1
 
< 0.1%
0.1989202288 1
 
< 0.1%
0.1948834084 1
 
< 0.1%
0.2263309198 1
 
< 0.1%
0.4613531079 1
 
< 0.1%
0.1069009247 1
 
< 0.1%
0.1154605386 1
 
< 0.1%
0.03877919054 1
 
< 0.1%
0.09896099179 1
 
< 0.1%
0.2187432348 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
-86.49091974 1
< 0.1%
-7.087424129 1
< 0.1%
-4.315260049 1
< 0.1%
-2.42673027 1
< 0.1%
-0.6929204033 1
< 0.1%
-0.6785950922 1
< 0.1%
-0.08343046992 1
< 0.1%
-0.05935228935 1
< 0.1%
-0.024309127 1
< 0.1%
-0.02203559071 1
< 0.1%
ValueCountFrequency (%)
4.380371393 1
< 0.1%
3.151067323 1
< 0.1%
3.144887151 1
< 0.1%
2.474894626 1
< 0.1%
2.457419059 1
< 0.1%
2.297622326 1
< 0.1%
2.263068403 1
< 0.1%
2.096018203 1
< 0.1%
2.005036387 1
< 0.1%
1.918373057 1
< 0.1%

debt_to_income_ratio
Real number (ℝ)

High correlation  Skewed  Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.32352152
Minimum-21.856784
Maximum93.567037
Zeros0
Zeros (%)0.0%
Negative16
Negative (%)0.2%
Memory size78.2 KiB
2025-04-14T22:19:52.410985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-21.856784
5-th percentile0.011474555
Q10.070354839
median0.17728227
Q30.37045131
95-th percentile0.92215009
Maximum93.567037
Range115.42382
Interquartile range (IQR)0.30009647

Descriptive statistics

Standard deviation1.5056987
Coefficient of variation (CV)4.6540914
Kurtosis2115.3613
Mean0.32352152
Median Absolute Deviation (MAD)0.12873978
Skewness40.565017
Sum3235.2152
Variance2.2671287
MonotonicityNot monotonic
2025-04-14T22:19:52.501787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3231938624 1
 
< 0.1%
0.04633241708 1
 
< 0.1%
0.01781460424 1
 
< 0.1%
0.4353905014 1
 
< 0.1%
0.09195809431 1
 
< 0.1%
0.1885049258 1
 
< 0.1%
0.1516143939 1
 
< 0.1%
0.3461478053 1
 
< 0.1%
0.2278953553 1
 
< 0.1%
0.002311830616 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
-21.85678392 1
< 0.1%
-13.02126372 1
< 0.1%
-9.710478476 1
< 0.1%
-7.706448239 1
< 0.1%
-3.161832038 1
< 0.1%
-1.630646846 1
< 0.1%
-1.321868865 1
< 0.1%
-1.285877694 1
< 0.1%
-1.269276165 1
< 0.1%
-1.072802633 1
< 0.1%
ValueCountFrequency (%)
93.56703712 1
< 0.1%
64.6786197 1
< 0.1%
58.53316604 1
< 0.1%
42.53090543 1
< 0.1%
29.94576354 1
< 0.1%
21.39039747 1
< 0.1%
14.4134637 1
< 0.1%
13.30860258 1
< 0.1%
12.62925574 1
< 0.1%
12.30746968 1
< 0.1%

spend_credit_score_interaction
Real number (ℝ)

High correlation 

Distinct9997
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1225995.4
Minimum-596012.62
Maximum2855282.9
Zeros0
Zeros (%)0.0%
Negative15
Negative (%)0.1%
Memory size78.2 KiB
2025-04-14T22:19:52.590402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-596012.62
5-th percentile530102.56
Q1942453.62
median1220242.5
Q31505750.7
95-th percentile1938993.9
Maximum2855282.9
Range3451295.5
Interquartile range (IQR)563297.06

Descriptive statistics

Standard deviation424059.82
Coefficient of variation (CV)0.34589021
Kurtosis0.021863137
Mean1225995.4
Median Absolute Deviation (MAD)282677.19
Skewness0.088409313
Sum1.2259954 × 1010
Variance1.7982673 × 1011
MonotonicityNot monotonic
2025-04-14T22:19:52.688419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1414466.08 2
 
< 0.1%
1118288.64 2
 
< 0.1%
1359400.23 2
 
< 0.1%
1658287.26 1
 
< 0.1%
771675.82 1
 
< 0.1%
915731.52 1
 
< 0.1%
1039820.76 1
 
< 0.1%
1630971.5 1
 
< 0.1%
1120411.95 1
 
< 0.1%
1382406.72 1
 
< 0.1%
Other values (9987) 9987
99.9%
ValueCountFrequency (%)
-596012.62 1
< 0.1%
-358146.35 1
< 0.1%
-200654.08 1
< 0.1%
-174465.39 1
< 0.1%
-152004 1
< 0.1%
-119326.21 1
< 0.1%
-77713.77 1
< 0.1%
-76222.02 1
< 0.1%
-64281.67 1
< 0.1%
-61581.5 1
< 0.1%
ValueCountFrequency (%)
2855282.85 1
< 0.1%
2812313.49 1
< 0.1%
2774149.4 1
< 0.1%
2757400.95 1
< 0.1%
2737130.32 1
< 0.1%
2714631.36 1
< 0.1%
2691449.76 1
< 0.1%
2557994.88 1
< 0.1%
2531788.64 1
< 0.1%
2530415.65 1
< 0.1%

Interactions

2025-04-14T22:19:47.635830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:34.749817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:35.688246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:36.666052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:37.843804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:38.763407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:39.820985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:40.801150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:41.769055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:42.868636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:43.795423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:44.687667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:45.798995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:46.713054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:47.703651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:34.815711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:35.758383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:36.728629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:37.906959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:38.829350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:39.888408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:40.866022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:41.836808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:42.934724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:43.856763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:44.753948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:45.861512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:46.777139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:47.777853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:34.902262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:35.832814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:36.797111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:37.975014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:38.902683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:39.962297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:40.936550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:41.910737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:43.004667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:43.922839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:44.826232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:45.930064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:46.847364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:47.843902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:34.964417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:35.897110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:36.857527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:38.036825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:38.966706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:40.026497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:40.998806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:41.975833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:43.066346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:43.982780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:44.889615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:45.991335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:46.909975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:47.913889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:35.029834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:35.964965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:36.920402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:38.099631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:39.034302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:40.094055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:41.063115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:42.042463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:43.129472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:44.044435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:44.955496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:46.053695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:46.973949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:47.987556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:35.097653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:36.036841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:36.988372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:38.168504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:39.103448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:40.164908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:41.133327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:42.238767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:43.198526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:44.111279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:45.029706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:46.124306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:47.042814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:48.061171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:35.165409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:36.111012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:37.058647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:38.238396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:39.176676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:40.237745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:41.203476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:42.313476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:43.267571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:44.178874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:45.102469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:46.192907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:47.111045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:48.130435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:35.229678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:36.178335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:37.383464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:38.301841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:39.242566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:40.305527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:41.301089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:42.382143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:43.331193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:44.240753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:45.170281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:46.255721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:47.176274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:48.205911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:35.300032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:36.253137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:37.454829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:38.372258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:39.317037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:40.389882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:41.372411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:42.454798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:43.401673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:44.309959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:45.244978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:46.325104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:47.247298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:48.274475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:35.362913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:36.320819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:37.519671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:38.434363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:39.382251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:40.455769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:41.437908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:42.522210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:43.464889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:44.371095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:45.458334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:46.387564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:47.311076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:48.339622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:35.423170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:36.383926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:37.579220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:38.495757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:39.542468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:40.519243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:41.498905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:42.587335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:43.524550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:44.428734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:45.522786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:46.446992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:47.371799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:48.414208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:35.492175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:36.457472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:37.648407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:38.564632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:39.616442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:40.591901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:41.569311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:42.661692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:43.598805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:44.496144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:45.593995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:46.519801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:47.441419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:48.480979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:35.554092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:36.523452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:37.709548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:38.626510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:39.681092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:40.658329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:41.632480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:42.726391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:43.661177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:44.556096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:45.657969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-04-14T22:19:47.502676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:48.549989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:35.617351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:36.591823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:37.772614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:38.691819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:39.747855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:40.725976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:41.697826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:42.794247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:43.724370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:44.618591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:45.725245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:46.642806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:47.565614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-04-14T22:19:52.765286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ageavg_monthly_spendclustercredit_card_limitcredit_scoredebt_to_income_ratioemployment_statusincomeloan_balanceloan_balance_logmissed_paymentsnum_credit_cardspca1pca2regionspend_credit_score_interactionspend_to_limit_ratio
age1.000-0.0050.048-0.005-0.005-0.0010.009-0.002-0.005-0.0050.005-0.0030.1440.2850.000-0.004-0.001
avg_monthly_spend-0.0051.0000.3140.018-0.001-0.0020.000-0.015-0.005-0.0050.0020.0130.283-0.4640.0080.9730.719
cluster0.0480.3141.0000.3650.1530.1770.0000.2230.4860.3810.1740.1040.3050.2320.0090.3450.000
credit_card_limit-0.0050.0180.3651.000-0.0020.0000.0150.0180.0060.0060.004-0.0090.018-0.2370.0000.018-0.624
credit_score-0.005-0.0010.153-0.0021.0000.0170.000-0.0120.0170.0170.015-0.0030.3220.4910.0000.2050.003
debt_to_income_ratio-0.001-0.0020.1770.0000.0171.0000.000-0.2790.9420.942-0.019-0.006-0.0960.3380.0120.003-0.001
employment_status0.0090.0000.0000.0150.0000.0001.0000.0000.0000.0060.0100.0190.0080.0000.0000.0000.000
income-0.002-0.0150.2230.018-0.012-0.2790.0001.0000.0010.001-0.019-0.007-0.534-0.0990.000-0.017-0.021
loan_balance-0.005-0.0050.4860.0060.0170.9420.0000.0011.0001.000-0.027-0.009-0.2660.3300.0040.000-0.007
loan_balance_log-0.005-0.0050.3810.0060.0170.9420.0060.0011.0001.000-0.027-0.009-0.2660.3300.0020.000-0.007
missed_payments0.0050.0020.1740.0040.015-0.0190.010-0.019-0.027-0.0271.000-0.0060.5660.1950.0000.0040.001
num_credit_cards-0.0030.0130.104-0.009-0.003-0.0060.019-0.007-0.009-0.009-0.0061.0000.262-0.4160.0000.0130.022
pca10.1440.2830.3050.0180.322-0.0960.008-0.534-0.266-0.2660.5660.2621.0000.0120.0110.3450.200
pca20.285-0.4640.232-0.2370.4910.3380.000-0.0990.3300.3300.195-0.4160.0121.0000.000-0.344-0.192
region0.0000.0080.0090.0000.0000.0120.0000.0000.0040.0020.0000.0000.0110.0001.0000.0140.000
spend_credit_score_interaction-0.0040.9730.3450.0180.2050.0030.000-0.0170.0000.0000.0040.0130.345-0.3440.0141.0000.701
spend_to_limit_ratio-0.0010.7190.000-0.6240.003-0.0010.000-0.021-0.007-0.0070.0010.0220.200-0.1920.0000.7011.000

Missing values

2025-04-14T22:19:48.662674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-14T22:19:48.840583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idageincomeemployment_statuscredit_card_limitnum_credit_cardscredit_scoreloan_balanceavg_monthly_spendmissed_paymentsregionloan_balance_logpca1pca2clusterspend_to_limit_ratiodebt_to_income_ratiospend_credit_score_interaction
0CUST000005642952.26Unemployed9026.700774.013882.232142.492West9.5384371.2101741.72822410.2373240.3231941658287.26
1CUST000016969507.31Freelancer16004.731754.06338.181373.801East8.7545050.0494320.99930000.0858320.0911861035845.20
2CUST000024672649.08Freelancer9410.596720.013418.141397.710Central9.504437-0.161978-0.76747820.1485090.1846951006351.20
3CUST000033250516.44Unemployed12264.082728.035450.652103.353West10.4759250.9094160.66312630.1714910.7017511531238.80
4CUST000046044564.56Retired12156.744802.015181.271528.360South9.6278840.9094920.77863610.1257110.3406501225744.72
5CUST000052592190.41Unemployed6459.932719.027489.931670.702Central10.221611-0.8863000.67220220.2585850.2981831201233.30
6CUST000063864549.92Retired4463.820647.06338.241736.660North8.754514-1.3879040.17250520.3889650.0981901123619.02
7CUST000075672719.36Employed12462.951741.05186.241334.914East8.5539571.1693751.39856500.1071020.071318989168.31
8CUST000083643558.48Employed6749.361676.04432.631489.230Central8.396974-0.5312460.17482320.2206150.1017601006719.48
9CUST000094035614.31Employed5007.455704.010136.991789.371Central9.2240451.169690-0.41543310.3572700.2846251259716.48
customer_idageincomeemployment_statuscredit_card_limitnum_credit_cardscredit_scoreloan_balanceavg_monthly_spendmissed_paymentsregionloan_balance_logpca1pca2clusterspend_to_limit_ratiodebt_to_income_ratiospend_credit_score_interaction
9990CUST099904461787.21Unemployed7620.691685.02269.031541.520South7.727548-0.8062240.15959620.2022540.0367231055941.20
9991CUST099913579691.40Employed4682.803658.01026.661796.560South6.935040-1.031192-0.96254720.3835690.0128831182136.48
9992CUST099924172282.12Employed10546.252708.04242.441703.003South8.3531300.7172780.16380000.1614640.0586921205724.00
9993CUST099935452803.19Freelancer11137.480693.019096.122216.892West9.8572930.3478800.74172200.1990300.3616401536304.77
9994CUST099944177917.02Employed7025.311673.06970.021629.363North8.8495170.0348280.50990020.2318940.0894531096559.28
9995CUST099955561829.60Employed8399.680773.050297.341087.022East10.825727-0.5459603.42697430.1293970.813470840266.46
9996CUST099965157877.36Employed14626.402660.05879.151995.953South8.6793381.019767-0.57066600.1364530.1015781317327.00
9997CUST099975750476.02Unemployed13478.491728.02441.331723.675South7.8007082.4112011.10885900.1278740.0483651254831.76
9998CUST099986463320.38Employed5546.733687.02914.591381.681North7.9778270.1254880.42757620.2490530.046029949214.16
9999CUST099993279929.25Freelancer9746.094687.010054.762187.703Central9.2159010.784293-1.01726200.2244460.1257941502949.90